Delineation of organs at risk (OARs) is a crucial step in surgical and treatment planning in brain cancer, where precise OARs volume delineation is required. However, this task is still often manually performed, which is time-consuming and prone to observer variability. To tackle these issues a deep learning approach based on stacking denoising auto-encoders has been proposed to segment the brainstem on magnetic resonance images in brain cancer context. Additionally to classical features used in machine learning to segment brain structures, two new features are suggested. Four experts participated in this study by segmenting the brainstem on 9 patients who underwent radiosurgery. Analysis of variance on shape and volume similarity metrics indicated that there were significant differences (p<0.05) between the groups of manual annotations and automatic segmentations. Experimental evaluation also showed an overlapping higher than 90% with respect to the ground truth. These results are comparable, and often higher, to those of the state of the art segmentation methods but with a considerably reduction of the segmentation time.
Breast metastasis from thyroid papillary carcinoma is an exceptional situation. Here, we present the diagnostic approach and the management of a 19-year-old woman with single breast metastasis from thyroid carcinoma. There was no extra thyroidal extension, neoplastic emboli, or lymph node invasion. The metastasis was revealed by whole-body radioactive 131I scan, explored by a fine-needle aspiration, and confirmed by elevated thyroglobulin in situ.
Radiation therapy has emerged as one of the preferred techniques to treat brain cancer patients. During treatment, a very high dose of radiation is delivered to a very narrow area. Prescribed radiation therapy for brain cancer requires precisely defining the target treatment area, as well as delineating vital brain structures which must be spared from radiotoxicity. Nevertheless, delineation task is usually still manually performed, which is inefficient and operator-dependent. Several attempts of automatizing this process have reported, however, marginal results when analyzing organs in the optic region. In this work we present a deep learning classification scheme based on augmented-enhanced features to automatically segment organs at risk in the optic region -optic nerves, optic chiasm, pituitary gland and pituitary stalk-. Fifteen MR images with various types of brain tumors were retrospectively collected to undergo manual and automatic segmentation. Mean Dice Similarity coefficients of 0.79, 0.83, 0.76 and 0.77, respectively, were reported in this study. Incorporation of proposed features yielded to improvements on the segmentation with respect to classical features. Compared with support vector machines, our method achieved better performance with less variation on the results, as well as a considerably reduction on the classification time. Performance of the proposed approach was also evaluated with respect to manual contours. In this case, results obtained from the automatic contours mostly lie on the variability of the observers. Additionally, in cases where our method was under performing with respect to manual raters, statistical analysis showed that there were not significant differences between them. These results suggest therefore that the proposed system is more accurate than other presented approaches, up to date, to segment these structures. The speed, reproducibility, and robustness of the process make the proposed deep learning-based classification system a valuable tool for assisting in the delineation task of small OARs in brain cancer.
A 24-year-old woman with an unresectable right mesencephalic pilocytic astrocytoma was treated with stereotaxic radiation therapy. Three months after a radiation therapy–induced bleeding, she presented a severe disabling low frequency rest and kinetic tremor involving the left upper limb, associated with dystonia, and a Holmes tremor was suspected. Thereby, we performed a 123I-FP-CIT SPECT (DATSCAN) that revealed a normal distribution of radiotracer over the left striatum, whereas no binding was seen in the right caudate and putamen. This pattern was consistent with a right severe nigrostriatal dopaminergic denervation due to an ipsilateral red nucleus injury.
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